Titlebook: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Pro; First International Hayit Greenspan,

Infuriate

FUSE

Recovering Physiological Changes in Nasal Anatomy with Confidence Estimateshanges make it challenging for non-deformable vision-based registration algorithms to find accurate alignments between CT image and intraoperative video. Large alignment errors can lead to injuries to critical structures. In this paper, we present a deformable video-CT registration that deforms the

元音

仲裁者

誰(shuí)在削木頭

Multi-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseasto automate the disease diagnosis based on clinical 2D US images since they provide partial anatomic information of the kidney and the 2D images of the same kidney may have heterogeneous appearance. To overcome this challenge, we develop a novel multi-instance deep learning method to build a robust

BRIBE

Data Augmentation from Sketch this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts. Simulated data could serve for data augmentation provided that its appearance was comparable to the actual appearance of intra-operative acquis

錯(cuò)誤

Conference proceedings 2019 Imaging, UNSURE 2019, and the 8.th. International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. ..For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and

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SSRIS

Conference proceedings 2019accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.?.

古董